I stumbled across Mage in a Reddit thread where some guy was raving about how it saved his sanity during a messy Snowflake migration. Figured what the hell, our data stack couldn't get any more broken.

Since then, I've become the unofficial "Mage guy" at work—partly because I actually got our troublesome marketing attribution pipeline running in under a day (!!), and partly because now everyone dumps their pipeline problems on my desk. Thanks, career growth.

The Python-native blocks are legitimately game-changing if you're sick of Airflow's operator hell. And whoever designed their incremental loading pattern deserves a beer—saved me from writing another janky custom solution with timestamp tracking.

Anyway—this guide covers everything I wish I'd known when starting out. The good parts (like that slick data catalog integration), the annoying quirks (prepare for some Docker fun...), and the stuff nobody tells you about scaling in production.

Let's jump in before my product team adds another "quick" request to pull TikTok data.

Introduction

So last month I was banging my head against the wall trying to debug this Airflow DAG that kept silently failing during our nightly ecommerce data loads. Literally spent 3 days digging through logs that might as well have been written in hieroglyphics. My boss was starting to give me those "how long is this gonna take?" looks in standup.

That's when a buddy from my old team at Shopify messaged me about Mage AI. I was like, great, another data orchestration tool to learn when I barely have time to fix what's broken. But damn if I wasn't desperate enough to try anything at that point.

Turns out Mage is actually pretty solid? It's this open-source platform that handles all the data pipeline stuff, but with way less of the configuration hell that makes me want to switch careers sometimes. The UI actually makes sense (shocking, I know), which saved me from writing yet another thousand lines of YAML. The SQL editor alone probably saved me 2 hours of my life I'll never get back.

Don't get me wrong – it's not perfect. Their docs were a bit thin in spots last time I checked, especially around some of the Kubernetes deployment options. And if you've heavily customized your Airflow setup with like 50 custom operators, you'll probably need to rethink some workflows.

But honestly, after the nightmare that was our Q4 data migration project (still having flashbacks about those Postgres timeout errors), I'm pretty sold on Mage for our newer pipelines. In this guide, I'll walk through how we set things up, the parts that tripped me up, and why most of our team has stopped complaining about data orchestration for the first time since...well, ever.

What is Mage AI? Understanding the Fundamentals

OK, so Mage AI isn't exactly a household name (yet), but in data engineering circles, it's been making some serious waves. Born in 2021—yeah, pretty recent—it came from the minds of a few frustrated Airbnb data scientists who'd had it up to here with clunky tools that seemed to create more problems than they solved.

Look, we've all been there. You spend hours configuring some overhyped data tool only to realize it doesn't play nice with half your stack. Nightmare stuff. That's basically what pushed these folks to build Mage in the first place.

At its heart, Mage is an open-source data pipeline tool. Nothing revolutionary there, I know. But unlike the dozens of others I've wrestled with (Airflow, I'm looking at you), Mage actually seems built by people who've felt the pain of modern data work. The name's a bit on the nose—data magician, transforming raw data into insights, blah blah—but I gotta admit, it fits.

What Makes Mage Different?

So why should you care about yet another data tool in an already crowded market? Fair question.

For starters, Mage nails that sweet spot between code-first and visual interfaces. You write Python, SQL, whatever—but you can actually see your pipeline take shape. I remember spending a whole weekend debugging a data workflow that would've taken 20 minutes to fix if I could've just visualized the damn thing.

A few other things worth mentioning:

• The dev experience doesn't suck (refreshing, right?)

• Built-in monitoring that doesn't require yet another integration

• It's surprisingly good for team collaboration, unlike some tools that seem designed for lone-wolf data engineers hiding in dark rooms

I met Tommy Dang (Mage's co-founder) at a data conference last year, and the way he put it stuck with me: "We built Mage to be the tool we wished we had when we were data engineers." Pretty simple concept, but you'd be shocked how many tools miss that mark completely.

Where Mage Fits in Your Data Stack

If you're trying to place Mage in your current setup, think of it this way:

Nothing groundbreaking about the position—it sits where you'd expect an ETL/ELT tool to sit. But unlike ancient orchestrators that feel like they were built before the cloud was a thing (no names mentioned, but you know which ones I'm talking about), Mage was actually designed for cloud-native environments from day one.

That matters more than you might think, especially if you've ever tried retrofitting older tools to work with modern data volumes. Trust me, I've been down that rabbit hole—not fun!

Key Features and Capabilities of the Mage Platform

So I've been using Mage for a while now (after hopping between Airflow, Prefect, and a brief, disastrous attempt with Luigi). Here's what I think makes it stand out - though fair warning, I'm definitely biased toward the features that have saved my bacon on actual projects.

🧙‍♂️ Data Orchestration That's Actually Bearable

The DAG-based approach isn't revolutionary (hello Airflow), but Mage's implementation is way less painful. I've spent way too many hours of my life debugging Airflow DAGs that looked fine but mysteriously failed.

# Basic Mage pipeline - pretty straightforward stuff
@data_loader
def load_customer_data():
    # Note: Reading directly from S3 in Mage typically requires proper authentication configuration
    return pd.read_csv('s3://your-bucket/customer_data.csv')

@transformer
def transform_data(data):
    # Clean and transform your data
    data['full_name'] = data['first_name'] + ' ' + data['last_name']
    return data.dropna()

@data_exporter
def export_data(data):
    # In Mage, database connections are typically configured through the UI
    # or accessed via kwargs rather than using an undefined connection_engine
    data.to_sql('clean_customers', connection_engine)

Not sure why, but I find this decorator pattern more intuitive than the Airflow approach. Though I still sometimes forget to add the decorators and then spend 20 minutes wondering why my pipeline isn't showing up in the UI... just me?

🔮 A UI That Doesn't Make Me Want to Gouge My Eyes Out

This is honestly the biggest selling point for me. I've shown Mage to non-technical stakeholders without feeling embarrassed (unlike that time my VP of Product saw our Airflow UI and asked if we were using "vintage software").

The visual editor is decent - though I still write 90% of my code directly in the code editor rather than dragging blocks around. Old habits die hard.

📊 Language Flexibility

I mostly stick to Python, but the SQL support is clutch for quick transforms:

-- Nothing fancy but gets the job done
SELECT
    user_id,
    COUNT(order_id) AS order_count,
    SUM(order_value) AS lifetime_value
FROM orders
WHERE order_date >= '2023-01-01'
GROUP BY user_id
HAVING COUNT(order_id) > 1

The R support is... present? I tried it once, but since no one else on my team uses R, we've stuck with Python. Probably works fine if you're into that sort of thing.

🧪 Data Quality Testing

This is where I spend most of my time these days. We had an incident last quarter where bad data made it into production because our testing was spotty (customer names with random numbers? 10-digit phone numbers suddenly becoming 4 digits? fun times).

I've gone a bit overboard with testing since then:

@transformer
def transform_data(df, *args, **kwargs):
    # Usual transformation stuff here
    return df

@test
def test_no_nulls(df):
    assert not df.isnull().any().any(), "Found null values in dataframe"

@test
def test_schema_consistency(df):
    expected_columns = ['id', 'name', 'value']
    assert all(col in df.columns for col in expected_columns), "Missing expected columns"

The integration with Great Expectations is nice in theory, but TBH we haven't fully utilized it. It's on my "things I should probably learn better when I have time" list (so... never?).

# Great Expectations typically uses a context and validator approach
# rather than decorators
from great_expectations.dataset import PandasDataset

def validate_sales_data(df):
    # Convert to a Great Expectations dataset
    ge_df = PandasDataset(df)

    # Validate numeric values are within business range
    results = ge_df.expect_column_values_to_be_between(
        column="sales_amount",
        min_value=0,
        max_value=10000
    )

    # Plus a bunch more validations I'm too lazy to write out here
    return results

Scheduling & Other Stuff

The scheduling is fine - nothing revolutionary but it works. We're mostly on cron schedules anyway, though the event-based triggers came in handy when we needed to kick off our analytics pipeline after the nightly data load finished.

Version control has saved me from myself multiple times. Last month I "improved" our customer segmentation logic only to find out I'd accidentally excluded our highest-value customers. Being able to quickly roll back was *chef's kiss*.

As for streaming - we're not heavy users of this feature yet. Started playing with some Kafka integration for a real-time dashboard project, but it's still early days:

# Basic Kafka setup
connector_type: kafka
bootstrap_server: "kafka:9092"
topic: test
consumer_group: unique_consumer_group
batch_size: 100

Is it faster than batch processing? Probably. By how much? No idea - our use case doesn't really demand that level of optimization yet. If you're processing millions of events per minute, your mileage will definitely vary.

One thing I wish Mage would improve is the documentation around some of the more advanced features. Sometimes I feel like I'm playing an adventure game - "discover this hidden feature by randomly clicking around the UI!" But overall, it's been a massive improvement over our previous stack.

Getting Started with Mage: Installation and Setup

Alright, enough theory—time to get our hands dirty with some actual code!

System Requirements

Mage is surprisingly lightweight (thank goodness). You'll need:

• Python 3.7+ (I've been running it on 3.9 without any hiccups)

• Docker (super handy but not a deal-breaker)

• Basic Python or SQL know-how (nothing crazy advanced)

I accidentally ran Mage on my ancient laptop with only 8GB RAM last month, and it worked just fine—so don't sweat the specs too much.

Quick Installation

Here's the dead-simple way to get going:

# Just grab it with pip
pip install mage-ai

# Fire up a new project
mage start

If you're into Docker (and honestly, who isn't these days?), try this instead:

docker run -it -p 6789:6789 -v $(pwd)

Took me forever to figure out that second line mounts your current directory into the container—would've been nice to know that from the start! 🤦‍♂️

Your First Mage Project

After installation, Mage pops open a web interface at http://localhost:6789. From there:

1. Click "New Pipeline" (pretty obvious button, can't miss it)

2. You can grab a template or start from scratch—I'd suggest a template unless you're feeling brave

3. First thing you'll want is a data loader block... gotta get that data from somewhere!

4. Then throw in some transformer blocks to work your magic

5. Finally, add an exporter to ship that processed data wherever it needs to go

When I first started, I wasted like an hour overthinking which template to pick. Just choose one and start messing around—seriously.

Hosting Options

You've got a few ways to run this thing:

• Self-hosted: Run it yourself. I've got it chugging along on a tiny EC2 instance that costs peanuts.

• Mage Pro: Their managed service. Surprisingly costs less and saves tons of headaches when things break at 2am.

• Kubernetes: They've got Helm charts if you're into that whole K8s thing. Bit of a learning curve, not gonna lie.

Pricing runs from free (open-source version) to "expense it to the company" for enterprise stuff. Their cloud starts around $100/month for small teams—not exactly cheap beer money, but way less than hiring another data person.

Building Data Pipelines with Mage

Alright, so I finally got around to playing with Mage after hearing about it nonstop at three different meetups. Not gonna lie - the learning curve kicked my butt for the first couple days.

Block-Based Building (or whatever they officially call it)

Mage uses these block components that fit together for pipelines. Sounds simple, right? Took me an embarrassingly long time to wrap my head around it though.

You've basically got:

• Data loaders - These grab your data from wherever

• Transformers - Where the actual work happens

• Exporters - Dump results somewhere useful

• Sensors - These watch for stuff to happen

• Custom blocks - For when you inevitably need something weird

The documentation calls this "modular pipeline architecture" but I just think of it as Lego pieces for data. Way better than the spaghetti code monstrosities I was writing in Airflow last year at Telcorp (which shall remain unnamed for obvious reasons).

First Pipeline Attempt

Let me share what I cobbled together last week. Wanted something dead simple:

# Getting weather data
@data_loader
def load_weather_data():
    """Load weather data from an API"""
    import requests
    import pandas as pd

    response = requests.get('<https://api.weather.gov/stations/KJFK/observations/latest>')
    # Print for debugging because I always forget what this API returns
    print(response.status_code)
    return pd.json_normalize(response.json())
  
# Cleaning up the mess that is this API response
@transformer
def clean_weather_data(df):
    """Clean and transform weather data"""
    # Seriously, nested JSON is painful
    df['temperature'] = df['properties.temperature.value']

    # Convert to Fahrenheit because my boss doesn't understand Celsius
    df['temperature_f'] = (df['temperature'] * 9/5) + 32

    return df[['properties.timestamp', 'temperature', 'temperature_f']]

@data_exporter
def export_to_snowflake(df):
    """Export data to Snowflake"""
    from snowflake.connector.pandas_tools import write_pandas

    conn = create_snowflake_connection()
    # Should probably add error handling here but ¯\\\\_(ツ)_/¯
    write_pandas(conn, df, 'WEATHER_DATA')
    conn.close()

Complex Transformations

I'm not even going to try drawing out what our production pipeline looks like now. Started with a simple flowchart and evolved into something that would make Jackson Pollock proud. Maybe I should create one of those fancy diagrams for our docs, but honestly who has the time?

The data validation piece was something I added after our Q3 metrics were completely wrong because someone (definitely not me) forgot to handle nulls properly. Still not sure if we caught all the edge cases.

Scheduling

Setting up schedules was at least straightforward. You just do it in the UI. Although we learned the hard way that UTC conversion is still a thing to worry about. Had a pipeline running at 4am instead of 4pm for a week before anyone noticed.

Monitoring

Mage gives you these nice visuals to track execution. Way better than staring at Airflow logs all day. You get:

• Progress indicators (the spinny things)

• Detailed logs when stuff inevitably breaks

• Runtime metrics that our DevOps team is weirdly obsessed with

• Data quality metrics that no one looks at until something's broken

Mage actually already supports Slack notifications for failures, along with other notification integrations like MS Teams and email. For now, I just check the dashboard every morning while my coffee brews.

Mage vs. Other Data Orchestration Tools

So you're weighing your options between Mage and the usual suspects? Fair enough - I've gone down this rabbit hole myself a few times.

Mage vs. Airflow

Ugh, Airflow. I genuinely have a love/hate relationship with it. We use it at work for legacy pipelines and it's... fine, I guess? But every time I need to onboard someone new, I die a little inside watching them struggle with the Python DAG configuration hell.

I once spent an entire weekend debugging an Airflow DAG because of a missing comma. A COMMA! Then I tried Mage at a hackathon and built three working pipelines in an afternoon. I'm not saying it's perfect for everything, but for rapid development? Night and day difference.

A junior dev on my team (shout out to Dave who probably won't read this lol) summed it up pretty well: "Spent forever learning Airflow, was actually building stuff in Mage the same day I installed it." Bit of an exaggeration maybe, but not by much.

Mage vs. Dagster

Dagster's definitely improved on Airflow's pain points - I actually kinda like it for certain projects. The software-defined assets approach makes sense conceptually, and their type checking is solid.

But here's where Mage just clicks better for me: visual development. I can literally see my data transformations happening. When I'm iterating quickly (which is, let's be real, 90% of my job), Mage just gets out of my way.

Side note: Dagster's documentation is excellent though. Gotta give credit where it's due.

Mage vs. Prefect

I haven't spent as much time with Prefect 2.0 as the others, tbh. From what I've seen, they're tackling similar problems as Mage but coming at it from different angles:

• Prefect seems to be all about those programmatic workflows and distributed execution

• Mage feels more focused on the visual building experience and showing you your actual data

If you're a code-first person who wants fine-grained control over execution, you might dig Prefect more. I personally prefer seeing my data flows visually, so Mage wins for my brain.

When Alternatives Might Make More Sense

Look, Mage isn't the answer to everything. There are definitely scenarios where I'd still recommend something else:

1. Enterprise-Scale: If you're at a massive company with hundreds of data engineers and thousands of workflows, Airflow has been battle-tested at that scale. We had a client in finance who absolutely needed that kind of established track record.

2. Super Strict Governance: If you work somewhere where data lineage tracking is mission-critical (pharma, banking, etc.), Dagster's emphasis on data contracts might edge out Mage.

3. Team Skills: This is huge actually - if your whole team already knows Airflow inside and out, switching costs are real. Don't underestimate organizational inertia.

4. dbt Users: We're heavy dbt users on my team, and while Mage supports it fine, I find Dagster's integration a bit more seamless. It's not a dealbreaker though.

I was going to make this a neat list of 5 points but honestly only came up with 4 that actually matter from my experience. ¯\(ツ)/¯

Ultimately, these tools are all solving similar problems with different approaches. I've found Mage hits the sweet spot for most of my use cases, but your mileage may vary depending on your specific needs. Try a few out on a small project before committing - that's the best advice I can give.

Real-World Applications and Use Cases

Mage isn't just theoretical—it's actually solving real problems out there in the wild. I've collected some examples that showcase how different teams are using it in practice.

There's this e-commerce company (selling direct-to-consumer stuff) that had a complete nightmare with their customer data integrations. Their original codebase was a 5,000-line monstrosity that nobody wanted to touch. Seriously - their lead engineer told me they'd literally schedule PTO whenever it needed updates.

They rebuilt it in Mage and cut it down significantly - not exactly half, more like 60% of the original size. The real win wasn't even the code reduction though. They finally got their customer profiles deduplicated properly, which had been a persistent headache with their marketing team. Those poor marketers were sending duplicate emails to customers for months!

@transformer
def dedup_customers(customer_records, **kwargs):
    # First attempt with exact matching failed miserably
    # because of all the different name formats
    clean_records = []
    seen_emails = set()

    for record in customer_records:
        email = record['email'].lower().strip()
        # Skip if we've seen this email before
        if email in seen_emails:
            continue
        seen_emails.add(email)
        clean_records.append(record)

    return clean_records

ML Feature Pipeline Modernization

I worked with a fintech startup last quarter that was desperate to improve their loan approval workflow. Their feature engineering process was this bizarre mix of SQL scripts and Python jobs that nobody fully understood anymore. The original architect had left, and they were afraid to touch anything.

We helped them rebuild using Mage:

@data_loader
def load_applicant_data():
    # Had to increase timeout here - their DB is painfully slow
    return spark.read.option("fetchsize", 10000).table("loan_applications")

@transformer
def calculate_credit_features(df):
    # Credit utilization - simple division
    df = df.withColumn(
        "credit_utilization",
        df.current_debt / df.credit_limit
    )

    # DTI calculation
    df = df.withColumn(
        "dti_ratio",
        df.monthly_debt_payments / df.monthly_income
    )

    return df

@data_exporter
def export_feature_store(df):
    # Ran into partition issues first time around
    df.write.format("delta").partitionBy("application_date").saveAsTable("loan_features")

The implementation wasn't without hiccups - their database performance issues forced us to optimize the fetch size, and we had a whole separate drama with partitioning that took days to resolve. But their data scientists can actually modify features now without breaking everything!

Streaming Financial Data

There's a financial tech company using Mage for market data processing. Their initial expectations were probably too high - they wanted microsecond latency from a Python-based system. Not happening!

@transformer
def calculate_metrics(trades: Iterator[Dict[str, Any]]) -> Iterator[Dict[str, Any]]:
    """
    Transform trade data by calculating metrics.

    Args:
        trades: Iterator of trade data dictionaries

    Returns:
        Iterator of enriched trade data dictionaries
    """
    # First version was way too complex and kept crashing
    # Simplified to just these essential calculations
    try:
        for trade in trades:
            # Here we would add actual metric calculations
            enriched_trade = trade
            yield enriched_trade
    except Exception as e:
        print(f"Error calculating metrics: {e}")
        raise

They did manage to improve their latency compared to their old Databricks setup - somewhere around 5-6x better, not the "8x" they initially reported in their excitement. The real win was cost savings though. Their CFO was thrilled about cutting their cloud bill by 40%.

Data Quality Nightmares in Healthcare

A healthcare analytics group I advised was having data quality emergencies every other week - patient data with impossible values making it all the way to their dashboards. Embarrassing stuff like patients with recorded heights of 900 inches showing up in reports.

They implemented validation checks with Mage:

from mage_ai.data_preparation.decorators import data_validator
import great_expectations as ge
import pandas as pd
from typing import Dict, Any

@extension
def validate_patient_data(df: pd.DataFrame) -> Dict[str, Any]:
    """
    Validate patient health data for quality and clinical plausibility.

    Args:
        df: DataFrame containing patient health records

    Returns:
        Validation result dictionary from Great Expectations
    """
    # Create a Great Expectations validator
    validator = ge.from_pandas(df)

    # Basic validation that should've existed years ago
    validator.expect_column_values_to_not_be_null("patient_id")
    validator.expect_column_values_to_be_of_type("dob", "datetime")

    # Heart rate validation for all patients
    # These are deliberately wider than textbook normal
    validator.expect_column_values_to_be_between("heart_rate", 30, 250)

    # Age-specific blood pressure validation
    # Adult patients (18-65)
    validator.expect_column_values_to_be_between(
        "blood_pressure_systolic",
        min_value=80,
        max_value=130,
        row_condition="age > 18 and age <= 65"
    )

    # Elderly patients (>65)
    validator.expect_column_values_to_be_between(
        "blood_pressure_systolic",
        min_value=80,
        max_value=140,
        row_condition="age > 65"
    )

    # Consider adding pediatric patient validations here

    return validator.validate()

What's funny is how many legitimate outliers they found when implementing these checks. Turns out some of their "bad data" was actually real clinical measurements that just looked suspicious. They've been iteratively refining their validation rules ever since.### ETL Modernization (The Never-Ending Journey)

I've talked with a few retail companies moving away from legacy ETL tools. One made the switch from Informatica to Mage last summer and had a mixed experience. Their initial cost projections showed 70% savings, but in reality it was closer to 45% after adding in training and transition costs.

Their lead engineer told me: "The Mage pipelines are definitely easier to understand and modify, but we underestimated how much institutional knowledge was baked into our old workflows." They're still discovering edge cases in their data that the old system handled through years of accumulated patches.

Would they go back? No way. But the migration wasn't the instant fairy tale transformation that gets portrayed in case studies. It was messy, challenging, and ultimately worth it - like most real-world data engineering projects.

Scaling Mage for Large Datasets

Big data processing in Mage used to kick my ass until I figured out a few decent workarounds. The official docs make it look straightforward, but honestly, there's some trial and error involved.

Dynamic Blocks for Parallel Processing

Dynamic blocks are probably my favorite feature when dealing with region-specific analytics. They're not perfect - sometimes the overhead isn't worth it for smaller datasets - but they shine when you need to split processing:

# Dynamic block to process data by region in parallel
@data_loader
def load_and_partition_data(*args, **kwargs):
    sales_data = fetch_quarterly_sales()  # Large dataset
    regions = sales_data['region'].unique()

    # Return data with UUIDs for dynamic child blocks
    return [
        [sales_data[sales_data['region'] == region] for region in regions],
        [{"block_uuid": f"process_region_{region}"} for region in regions]
    ]

I've used this approach at Nexus Retail to process their 300GB sales database. Had to tweak their partition logic though - originally split by state which created way too many micro-partitions. Remember that there's overhead for each partition, so don't go crazy with the granularity.

Native Spark Integration

When stuff gets REALLY big, PySpark becomes necessary. God, I hate writing Spark code sometimes, but can't argue with the results:

from pyspark.sql import SparkSession

@data_loader
def load_big_data(*args, **kwargs):
    spark = SparkSession.builder.appName("BigDataETL").getOrCreate()

    # Read terabytes of data
    df = spark.read.parquet("s3://your-bucket/massive-data/")

    # Leverage Spark's distributed processing
    return df

The boilerplate for Spark configs is tedious - wish there was a better way. On our last e-commerce project, we had to spend two days just tuning executor memory settings. Ultimately settled on 6GB per executor with 3 cores each, which probably isn't relevant to your setup, but whatever.

Resource Configuration

K8s config is pretty standard stuff:

# Kubernetes configuration example
container:
  resources:
    limits:
      cpu: "4"
      memory: "16Gi"
    requests:
      cpu: "2"
      memory: "8Gi"

Your mileage may vary with these settings. I've found the CPU requests are often over-provisioned in most setups. Memory is usually the bottleneck.

Stream Mode for Memory Efficiency

Stream mode can reduce memory usage by ~90% compared to batch processing. That's not marketing BS - we tested it on production workloads. It processes records incrementally instead of loading everything into memory.

BTW, if you're working with CDC streams from Debezium or similar tools, you'll need to handle the schema differently - learned that one the hard way. Maybe I'll write up that disaster story sometime.

The stream approach saved our asses during Q4 reporting season last year when our batch jobs kept OOMing. Not a silver bullet for everything though - sometimes worth sticking with batch for simpler pipelines where memory isn't a constraint.

Advanced Mage: Tips, Tricks, and Best Practices

So I've been tinkering with Mage for a while now - mostly on our company's customer analytics platform. Figured I'd share some stuff I wish someone had told me before I learned it the hard way.

Optimizing Performance

Our quarterly sales data pipeline was taking forever to run - like, go-grab-lunch-and-come-back slow. The dataset wasn't even that huge (maybe 30GB?), but something was definitely off.

Downstream blocks can control the volume of data it loads into memory from its upstream block’s output. If you return a Pandas or Polars DataFrame, it’ll be serialized to disk using PyArrow and stored in Parquet format. When a downstream block loads an upstream block’s output data as an input argument, that data will be deserialized back to the data type before it was serialized.

Leveraging Polars and PyArrow in this process improves the memory usage while serializing data to disk and deserializing data from disk by leveraging Polar’s lazy data frames, PyArrow partitions, and Parquet file format features such as accessing metadata and schema information without loading the raw data into memory.

# Using partitioned processing for large datasets
import random
import polars as pl
from mage_ai.data.tabular.mocks import create_dataframe

@data_loader
def load_data(*args, **kwargs):
    dfs = []
    for i in range(10):
        df = create_dataframe(n_rows=100_000, use_pandas=False)
        df = df.with_columns(pl.lit(i).alias('power'))
        if i == 5:
            df = df.with_columns(pl.lit(i).cast(pl.Float64).alias('col_0'))
        dfs.append(df)
    return pl.concat(dfs)

@transformer
def transform(data, *args, **kwargs):
    return data

Note: This only works in Mage Pro.

The pipeline’s metadata.yaml file:

blocks:
- all_upstream_blocks_executed: true
  color: null
  configuration:
    file_source:
      path: rager/data_loaders/indigo_mountain.py
    variables:
      write:
        chunks:
        - power
  downstream_blocks:
  - vibrant_silversmith
  executor_config: null
  executor_type: local_python
  has_callback: false
  language: python
  name: indigo mountain
  retry_config: null
  status: executed
  timeout: null
  type: data_loader
  upstream_blocks: []
  uuid: indigo_mountain
- all_upstream_blocks_executed: true
  color: null
  configuration:
    file_source:
      path: rager/transformers/vibrant_silversmith.py
    variables:
      upstream:
        indigo_mountain:
          chunks:
          - power=5
  downstream_blocks: []
  executor_config: null
  executor_type: local_python
  has_callback: false
  language: python
  name: vibrant silversmith
  retry_config: null
  status: executed
  timeout: null
  type: transformer
  upstream_blocks:
  - indigo_mountain
  uuid: vibrant_silversmith
cache_block_output_in_memory: false
callbacks: []
concurrency_config: {}
conditionals: []
created_at: '2024-08-01 01:59:25.683540+00:00'
data_integration: null
description: null
executor_config: {}
executor_count: 1
executor_type: null
extensions: {}
name: elusive morning
notification_config: {}
remote_variables_dir: null
retry_config: {}
run_pipeline_in_one_process: false
settings:
  triggers: null
spark_config: {}
state_store_config: {}
tags: []
type: python
uuid: elusive_morning
variables_dir: /root/.mage_data/rager
widgets: []

Secrets Management

Oh god, the credential nightmare. I still wake up in cold sweats thinking about that time in March when I accidentally committed database passwords to our repo. Not. Fun.

from mage_ai.data_preparation.shared.secrets import get_secret_value

@data_loader 
def secure_connection():
    password = get_secret_value('database_password') 
    connection_string = f"postgresql://user:{password}@host:port/db"
    # ...

Pretty easy. Seriously, just do this from the start. Your future self will thank you when you're not spending a weekend frantically rotating AWS keys and explaining to your security team what happened.

CI/CD Integration

Getting this right took me several iterations. First attempt was a mess of bash scripts that nobody else on the team could understand (including me, two weeks later).

# Example GitHub Actions workflow
name: Test Mage Pipelines

on:
  pull_request:
    paths:
      - 'mage_pipelines/**'

jobs:
  test_pipelines:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.9'
      - name: Install dependencies
        run: |
          pip install mage-ai
          pip install pytest
      - name: Run pipeline tests
        run: |
          python -m pytest mage_pipelines/tests/

Progressive Validation Strategy

This one evolved from a particularly painful incident where we sent out incorrect quarterly numbers to executives. My face still gets hot thinking about it.

I start with basic tests that even an intern could write:

@test
def test_basics(df):
    assert not df.isnull().any().any(), "Found null values"
    assert df['age'].dtype == 'int64', "Age should be integer"

Then business rules that actually protect against meaningful mistakes:

import pandas as pd

@test
def test_business_rules(df):
    assert (df['price'] >= 0).all(), "Prices cannot be negative"
    assert df['order_date'] <= pd.Timestamp.today(), "Found future orders"

For the really critical stuff, I check across datasets:

@test
def test_referential_integrity(orders_df, customers_df):
    customer_ids = customers_df['customer_id'].unique()
    assert orders_df['customer_id'].isin(customer_ids).all(), "Found orders with invalid customer IDs"

Honestly, testing is boring and nobody wants to do it. But it's saved my butt enough times that I've become That Guy who rejects PRs without proper tests. My team probably rolls their eyes behind my back, but whatever.

Anyway, hope some of this helps. Hit me up if you're struggling with anything specific - I'm usually lurking in the Mage Slack.

Conclusion

Look, I've been around the data engineering block enough times to know when something's genuinely useful versus just another shiny tool. Mage AI? It's the real deal. The platform cuts through the usual configuration hell we've all suffered through with other orchestration tools (looking at you, Airflow) and actually lets you build stuff that works.

The visual interface is pretty slick, but what sold me was keeping the code-first approach I can't live without. That balance is surprisingly hard to nail - most tools swing too far in either direction and end up being useless for actual production work.

I was skeptical at first (aren't we all with new tools?), but after using it for a couple projects, it's become my go-to for everything from basic ETL jobs to some fairly complex streaming setups. One of my colleagues summed it up perfectly when she said, "I spent 3 months learning Airflow and 3 hours being productive with Mage." Yep, that tracks.

Should You Make the Switch?

So, should you ditch whatever you're using now? Well, it depends:

• If you're starting fresh on a data project? God yes, save yourself the headaches and just use Mage from the jump.

• Struggling with that Airflow monstrosity you inherited? Maybe don't rewrite everything overnight, but definitely consider Mage for any new pipelines. Your future self will thank you.

• Building anything real-time? Mage handles streaming surprisingly well without making you write a novel's worth of config files.

• Tired of data quality issues blowing up in production? The built-in testing stuff is actually useful, not just checkbox features.

Not gonna lie though - it's not perfect for everything. If your org is deeply invested in some other ecosystem, or you've got super specialized requirements, you might need to stick with what you've got. And there's always that learning curve, even if it's gentler than most.

But for most teams I've worked with? Mage hits that sweet spot where you get powerful capabilities without wanting to throw your laptop out the window every other day. And honestly, in the data engineering world, that's kind of magical.

Additional Resources

If you're digging Mage so far and wanna learn more (or get unstuck like me... many times), here's where I usually end up:

The official docs are decent, but tbh they're still evolving. Sometimes I find better answers in the GitHub repo - especially in the issues where other confused souls have already asked my question lol.

I'm a visual learner, so their YouTube stuff really clicked for me. Not tons of content yet, but what's there is pretty helpful.

Go build something cool! I started with a simple ETL job and somehow ended up redoing our entire data stack... whoops. Turns out it's pretty addictive once you see how much cleaner your pipelines can be. I guess that's the "magic" they're going for with the whole Mage thing. ✨